






"""simple docstring"""
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def 							A						(				__snake_case: int ,     __snake_case: List[str]  )    ->		Optional[Any]:

       """simple docstring"""





       assert isinstance(__snake_case ,     __snake_case  )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory' ,     [False, True]  )
def 							A						(				__snake_case: str ,     __snake_case: Optional[Any] ,     __snake_case: Any  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              __magic_name__      				=    ParquetDatasetReader(__snake_case ,     cache_dir=__snake_case ,     keep_in_memory=__snake_case  ).read()
       _check_parquet_dataset(__snake_case ,     __snake_case  )


@pytest.mark.parametrize(
    'features' ,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ] ,     )
def 							A						(				__snake_case: Optional[int] ,     __snake_case: str ,     __snake_case: List[str]  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       __magic_name__      				=    features.copy() if features else default_expected_features
       __magic_name__      				=    (
           Features({feature: Value(__snake_case  ) for feature, dtype in features.items()}  ) if features is not None else None
       )
       __magic_name__      				=    ParquetDatasetReader(__snake_case ,     features=__snake_case ,     cache_dir=__snake_case  ).read()
       _check_parquet_dataset(__snake_case ,     __snake_case  )


@pytest.mark.parametrize('split' ,     [None, NamedSplit('train'  ), 'train', 'test']  )
def 							A						(				__snake_case: Dict ,     __snake_case: List[Any] ,     __snake_case: Any  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       __magic_name__      				=    ParquetDatasetReader(__snake_case ,     cache_dir=__snake_case ,     split=__snake_case  ).read()
       _check_parquet_dataset(__snake_case ,     __snake_case  )
       assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type' ,     [str, list]  )
def 							A						(				__snake_case: List[Any] ,     __snake_case: Dict ,     __snake_case: List[str]  )    ->		Optional[int]:

       """simple docstring"""





       if issubclass(__snake_case ,     __snake_case  ):
              __magic_name__      				=    parquet_path
       elif issubclass(__snake_case ,     __snake_case  ):
              __magic_name__      				=    [parquet_path]
       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       __magic_name__      				=    ParquetDatasetReader(__snake_case ,     cache_dir=__snake_case  ).read()
       _check_parquet_dataset(__snake_case ,     __snake_case  )


def 							A						(				__snake_case: Tuple ,     __snake_case: List[str] ,     __snake_case: Optional[Any]=("train",)  )    ->		Optional[int]:

       """simple docstring"""





       assert isinstance(__snake_case ,     __snake_case  )
       for split in splits:
              __magic_name__      				=    dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory' ,     [False, True]  )
def 							A						(				__snake_case: int ,     __snake_case: Optional[Any] ,     __snake_case: Dict  )    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              __magic_name__      				=    ParquetDatasetReader(
                  {'train': parquet_path} ,     cache_dir=__snake_case ,     keep_in_memory=__snake_case  ).read()
       _check_parquet_datasetdict(__snake_case ,     __snake_case  )


@pytest.mark.parametrize(
    'features' ,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ] ,     )
def 							A						(				__snake_case: Tuple ,     __snake_case: Tuple ,     __snake_case: Optional[Any]  )    ->		Optional[int]:

       """simple docstring"""





       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       __magic_name__      				=    features.copy() if features else default_expected_features
       __magic_name__      				=    (
           Features({feature: Value(__snake_case  ) for feature, dtype in features.items()}  ) if features is not None else None
       )
       __magic_name__      				=    ParquetDatasetReader({'train': parquet_path} ,     features=__snake_case ,     cache_dir=__snake_case  ).read()
       _check_parquet_datasetdict(__snake_case ,     __snake_case  )


@pytest.mark.parametrize('split' ,     [None, NamedSplit('train'  ), 'train', 'test']  )
def 							A						(				__snake_case: Optional[Any] ,     __snake_case: str ,     __snake_case: List[str]  )    ->		Tuple:

       """simple docstring"""





       if split:
              __magic_name__      				=    {split: parquet_path}
       else:
              __magic_name__      				=    'train'
              __magic_name__      				=    {'train': parquet_path, 'test': parquet_path}
       __magic_name__      				=    tmp_path / 'cache'
       __magic_name__      				=    {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
       __magic_name__      				=    ParquetDatasetReader(__snake_case ,     cache_dir=__snake_case  ).read()
       _check_parquet_datasetdict(__snake_case ,     __snake_case ,     splits=list(path.keys()  )  )
       assert all(dataset[split].split == split for split in path.keys()  )


def 							A						(				__snake_case: str ,     __snake_case: Tuple  )    ->		Any:

       """simple docstring"""





       __magic_name__      				=    ParquetDatasetWriter(__snake_case ,     tmp_path / 'foo.parquet'  )
       assert writer.write() > 0
       __magic_name__      				=    pq.ParquetFile(tmp_path / 'foo.parquet'  )
       __magic_name__      				=    pf.read()
       assert dataset.data.table == output_table


def 							A						(				__snake_case: List[Any] ,     __snake_case: Union[str, Any]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    str(shared_datadir / 'test_image_rgb.jpg'  )
       __magic_name__      				=    {'image': [image_path]}
       __magic_name__      				=    Features({'image': Image()}  )
       __magic_name__      				=    Dataset.from_dict(__snake_case ,     features=__snake_case  )
       __magic_name__      				=    ParquetDatasetWriter(__snake_case ,     tmp_path / 'foo.parquet'  )
       assert writer.write() > 0

       __magic_name__      				=    Dataset.from_parquet(str(tmp_path / 'foo.parquet'  )  )
       assert dataset.features == reloaded_dataset.features

       __magic_name__      				=    ParquetDatasetReader(str(tmp_path / 'foo.parquet'  ) ,     streaming=__snake_case  ).read()
       assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    'feature, expected' ,     [
        (Features({'foo': Value('int32'  )}  ), None),
        (Features({'image': Image(), 'foo': Value('int32'  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ] ,     )
def 							A						(				__snake_case: Tuple ,     __snake_case: Dict  )    ->		str:

       """simple docstring"""





       assert get_writer_batch_size(__snake_case  ) == expected







"""simple docstring"""
def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit + 1
       __magic_name__      				=    [0] * limit
       for first_term in range(1 ,     __snake_case  ):
              for n in range(__snake_case ,     __snake_case ,     __snake_case  ):
                     __magic_name__      				=    first_term + n / first_term
                     if common_difference % 4:  # d must be divisble by 4
                            continue
                     else:
                            common_difference /= 4
                            if (
                                first_term > common_difference
                                and first_term < 4 * common_difference
                            ):  # since x,y,z are positive integers
                                   frequency[n] += 1  # so z>0 and a>d ,also 4d<a

       __magic_name__      				=    sum(1 for x in frequency[1:limit] if x == 1_0  )

       return count


if __name__ == "__main__":
 print(f"""{solution() = }""")